Deep Learning on emotion detection<\/strong><\/h5>\n
Emotion Detection and Recognition from the text is a recent field of research that is closely related to Sentiment Analysis. Sentiment Analysis aims to detect positive, neutral, or negative feelings from the text, whereas Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness,<\/em> and surprise<\/em>. Emotion detection may have useful application<\/p>\n
Opinion mining<\/strong> (sometimes known as sentiment analysis<\/strong> or emotion AI<\/strong>) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study effective states and subjective information. <\/p><\/blockquote>\n
Some challenging examples<\/h5>\n
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I do not dislike cabin cruisers. (Negation handling)<\/li>\n
Disliking watercraft is not really my thing. (Negation, inverted word order)<\/li>\n
Sometimes I really hate RIBs. (Adverbial modifies the sentiment)<\/li>\n
I’d really truly love going out in this weather! (Possibly sarcastic)<\/li>\n
Chris Craft is better looking than Limestone. (Two brand names, identifying a target of attitude is difficult).<\/li>\n
Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).<\/li>\n
The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).<\/li>\n
You should see their decadent dessert menu. (the Attitudinal term has shifted polarity recently in certain domains)<\/li>\n
I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorize)<\/li>\n
Next week’s gig will be right koide9! (Newly minted terms can be highly attitudinal but volatile in polarity and often out of known vocabulary.)<\/li>\n<\/ul>\n
The Hangzhou No. 11 Middle School is trialing the tech as part of its \u201cSmart Classroom Behaviour Management System.\u201d The three cameras placed above the blackboard analyze pupils by scanning them every 30 seconds and determining if they\u2019re happy, confused, angry, surprised, fearful, or disgusted. They are also designed to log six types of student behaviors: reading, writing, hand raising, standing up, listening to the teacher, and leaning on the desk.<\/p>\n
Hangzhou Network reports that the system can alert a teacher if a student\u2019s attention level falls below a certain point. Not only can it be used as a teaching aid, but it\u2019s also able to monitor class attendances by checking students\u2019 faces against a database.<\/p>\n
Unsurprisingly, the use of the cameras has raised privacy questions as they are recording minors, but school vice principle Zhang Guanchao says the images themselves are not saved and the results are stored on a local server instead of the cloud.<\/p>\n